Clinical coding of hospital admissions can erroneously omit diagnosis and procedure codes. A consequence of these omissions is that the condition and treatment of the patient are not fully captured by the entered codes, which can then also impact hospital revenue. One way to prevent these errors is through a real-time recommender system which suggests the addition of codes at the point of coding when it appears they have been omitted. Association analysis uncovers patterns between codes, forming a basis for coding recommendations. Combining association analysis with manual expert validation produces more useful recommendations (we refer to this as the expert validated list), but is labour-intensive. In this study, we propose an approach using Bayesian Networks to determine the conditional relationships between codes. Performance is evaluated using a testing strategy which simulates errors through the random removal of codes from episodes of patient care and counts how many of the removed codes are recommended to coders by each recommender. Performance is also based on how many recommended codes were not removed (superfluous recommendations) which we seek to minimise. We develop a recommender system which generates 96% of the number of correct recommendations produced by the expert validated list, while having 68% fewer superfluous recommendations. Our proposed methodology provides a high performance recommender while reducing dependence on labour-intensive effort by clinical coding experts.

医院入院的临床编码可能会错误地省略诊断和程序编码。这些遗漏的结果是，所输入的代码无法完全记录患者的病情和治疗情况，进而影响医院的收入。防止这些错误的一种方法是通过实时推荐系统，该系统建议在编码点添加已被忽略的编码点。关联分析揭示了代码之间的模式，为编码建议奠定了基础。将关联分析与手动专家验证相结合会产生更有用的建议（我们将其称为专家验证列表），但劳动强度大。在这项研究中，我们提出了一种使用贝叶斯网络来确定代码之间的条件关系的方法。使用一种测试策略对性能进行评估，该策略通过从患者护理情节中随机删除代码来模拟错误，并计算每个推荐者推荐给代码员的删除代码数量。效果还取决于我们要尽量减少的推荐代码（多余的推荐）数量。我们开发了一个推荐器系统，该系统可生成经过专家验证的清单所产生的正确推荐数量的96％，同时减少了68％的多余推荐。我们提出的方法可提供高性能的建议，同时减少临床编码专家对劳动密集型工作的依赖。